Detection of glaucoma using retinal fundus images: A comprehensive review Article Swipe
Amsa Shabbir
,
Aqsa Rasheed
,
Huma Shehraz
,
Aliya Saleem
,
Bushra Zafar
,
Muhammad Sajid
,
Nouman Ali
,
Saadat Hanif Dar
,
Tehmina Shehryar
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3934/mbe.2021106
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3934/mbe.2021106
Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3934/mbe.2021106
- OA Status
- gold
- Cited By
- 67
- References
- 169
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3134067449
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3134067449Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3934/mbe.2021106Digital Object Identifier
- Title
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Detection of glaucoma using retinal fundus images: A comprehensive reviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
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Amsa Shabbir, Aqsa Rasheed, Huma Shehraz, Aliya Saleem, Bushra Zafar, Muhammad Sajid, Nouman Ali, Saadat Hanif Dar, Tehmina ShehryarList of authors in order
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https://doi.org/10.3934/mbe.2021106Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3934/mbe.2021106Direct OA link when available
- Concepts
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Glaucoma, Fundus (uterus), Artificial intelligence, Optometry, Computer science, Image processing, Medicine, Computer vision, Ophthalmology, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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67Total citation count in OpenAlex
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2025: 8, 2024: 10, 2023: 22, 2022: 22, 2021: 5Per-year citation counts (last 5 years)
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169Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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